Automatica最新文献

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Probabilistic predictability of stochastic dynamical systems

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-30 DOI: 10.1016/j.automatica.2025.112160

Tao Xu, Yushan Li, Jianping He

To assess the quality of a probabilistic prediction for stochastic dynamical systems (SDSs), scoring rules assign a numerical score based on the predictive distribution and the measured state. In this paper, we propose an

ϵ

-logarithm score that generalizes the celebrated logarithm score by considering a neighborhood with radius

ϵ

. We characterize the probabilistic predictability of an SDS by optimizing the expected score over the space of probability measures. We show how the probabilistic predictability is quantitatively determined by the neighborhood radius, the differential entropies of process noises, and the system dimension. Given any predictor, we provide approximations for the expected score with an error of scale

O (ϵ)

. In addition to the expected score, we also analyze the asymptotic behaviors of the score on individual trajectories. Specifically, we prove that the score on a trajectory can converge to the expected score when the process noises are independent and identically distributed. Moreover, the convergence speed against the trajectory length

T

is of scale

O (T^{- \frac{1}{2}})

in the sense of probability. Finally, numerical examples are given to elaborate the results.

{"title":"Probabilistic predictability of stochastic dynamical systems","authors":"Tao Xu, Yushan Li, Jianping He","doi":"10.1016/j.automatica.2025.112160","DOIUrl":"10.1016/j.automatica.2025.112160","url":null,"abstract":"<div><div>To assess the quality of a probabilistic prediction for stochastic dynamical systems (SDSs), scoring rules assign a numerical score based on the predictive distribution and the measured state. In this paper, we propose an <span><math><mi>ϵ</mi></math></span>-logarithm score that generalizes the celebrated logarithm score by considering a neighborhood with radius <span><math><mi>ϵ</mi></math></span>. We characterize the probabilistic predictability of an SDS by optimizing the expected score over the space of probability measures. We show how the probabilistic predictability is quantitatively determined by the neighborhood radius, the differential entropies of process noises, and the system dimension. Given any predictor, we provide approximations for the expected score with an error of scale <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>ϵ</mi><mo>)</mo></mrow></mrow></math></span>. In addition to the expected score, we also analyze the asymptotic behaviors of the score on individual trajectories. Specifically, we prove that the score on a trajectory can converge to the expected score when the process noises are independent and identically distributed. Moreover, the convergence speed against the trajectory length <span><math><mi>T</mi></math></span> is of scale <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>T</mi></mrow><mrow><mo>−</mo><mfrac><mrow><mn>1</mn></mrow><mrow><mn>2</mn></mrow></mfrac></mrow></msup><mo>)</mo></mrow></mrow></math></span> in the sense of probability. Finally, numerical examples are given to elaborate the results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"174 ","pages":"Article 112160"},"PeriodicalIF":4.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A neural network-based approach to hybrid systems identification for control

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-30 DOI: 10.1016/j.automatica.2025.112130

Filippo Fabiani , Bartolomeo Stellato , Daniele Masti , Paul J. Goulart

We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the model obtained is also suitable for optimal control design. We adopt a neural network (NN) architecture that, once suitably trained, yields a hybrid system with continuous piecewise-affine (PWA) dynamics that is differentiable with respect to the network’s parameters, thereby enabling the use of derivative-based training procedures. We show that a careful choice of our NN’s weights produces a hybrid system model with structural properties that are highly favorable when used as part of a finite horizon optimal control problem (OCP). Specifically, we rely on available results to establish that optimal solutions with strong local optimality guarantees can be computed via nonlinear programming (NLP), in contrast to classical OCPs for general hybrid systems which typically require mixed-integer optimization. Besides being well-suited for optimal control design, numerical simulations illustrate that our NN-based technique enjoys very similar performance to state-of-the-art system identification methods for hybrid systems and it is competitive on nonlinear benchmarks.

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引用次数: 0

Accelerated secondary frequency regulation and active power sharing for islanded microgrids with external disturbances: A fully distributed approach

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-30 DOI: 10.1016/j.automatica.2025.112146

Boda Ning , Qing-Long Han , Zongyu Zuo , Lei Ding

Islanded microgrids face some challenges in maintaining stable frequency and sharing proper power among distributed generators (DGs) in the presence of external disturbances. This paper develops a novel fully distributed approach to achieve accelerated secondary frequency regulation (FR) and active power sharing (APS) in islanded microgrids, which enhances system performance and robustness against external disturbances. The proposed control strategy combines advanced consensus algorithms with distributed secondary control loops, eliminating the requirement for a central control unit thereby improving the scalability. Particularly, the fully distributed feature of the proposed control strategy can be understood from two aspects. On one hand, the controller itself is not using global information of (1) communication topology, such as the second smallest eigenvalue of its Laplacian matrix; and (2) the total number of DGs in the microgrid. On the other hand, the estimated settling time is independent of the aforementioned global information. Therefore, the proposed fully distributed control scheme has the potential of becoming a promising solution for the resilient and efficient management of large-scale islanded microgrids. The effectiveness of the designed controllers is validated through numerical examples, demonstrating superior performance in terms of FR, APS, and transient response under various operating conditions.

{"title":"Accelerated secondary frequency regulation and active power sharing for islanded microgrids with external disturbances: A fully distributed approach","authors":"Boda Ning , Qing-Long Han , Zongyu Zuo , Lei Ding","doi":"10.1016/j.automatica.2025.112146","DOIUrl":"10.1016/j.automatica.2025.112146","url":null,"abstract":"<div><div>Islanded microgrids face some challenges in maintaining stable frequency and sharing proper power among distributed generators (DGs) in the presence of external disturbances. This paper develops a novel fully distributed approach to achieve accelerated secondary frequency regulation (FR) and active power sharing (APS) in islanded microgrids, which enhances system performance and robustness against external disturbances. The proposed control strategy combines advanced consensus algorithms with distributed secondary control loops, eliminating the requirement for a central control unit thereby improving the scalability. Particularly, the fully distributed feature of the proposed control strategy can be understood from two aspects. On one hand, the controller itself is not using global information of (1) communication topology, such as the second smallest eigenvalue of its Laplacian matrix; and (2) the total number of DGs in the microgrid. On the other hand, the estimated settling time is independent of the aforementioned global information. Therefore, the proposed fully distributed control scheme has the potential of becoming a promising solution for the resilient and efficient management of large-scale islanded microgrids. The effectiveness of the designed controllers is validated through numerical examples, demonstrating superior performance in terms of FR, APS, and transient response under various operating conditions.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"174 ","pages":"Article 112146"},"PeriodicalIF":4.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strong well-posedness of the regular linear-quadratic problems: Stabilizable case

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-30 DOI: 10.1016/j.automatica.2025.112145

Renren Zhang

This paper delves into an open problem within the field of optimal control: the strong well-posedness of the free-endpoint regular indefinite linear quadratic optimal control (LQ). The problem is closely intertwined with the existence of a solution possessing specific properties to an algebraic Riccati equation or inequality. In this paper, some explicit necessary and/or sufficient conditions for the strong well-posedness of the stabilizable case are given, by investigating the existence of a special solution of an algebraic Riccati equation (ARE) corresponding to the LQ and the properties of the minimum solution of an ARE constructed by the controllable part of the system. Additionally, a comparison of the existing sufficient criteria in the literature is provided.

引用次数: 0

Mean–Variance optimization in discrete-time decision processes with general utility function

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-30 DOI: 10.1016/j.automatica.2025.112142

Nicole Bäuerle , Anna Jaśkiewicz , Andrzej S. Nowak

We study general discrete-time Mean–Variance problems in a non-Markovian setting. The utility is a general, continuous function which may depend on the entire history of the process. It contains many recursive utility functions with non-linear aggregator as special cases. Under some continuity and compactness assumptions on the model data, we establish the existence of persistently optimal deterministic policies. For finite horizon problems this also yields a recursive solution algorithm. The theory which we develop here goes beyond Mean–Variance models and may be applied, e.g., to Optimized Certainty Equivalents. The Mean–Variance optimization framework is also applied to a multi-stage portfolio analysis with constraints on short selling.

引用次数: 0

Safe zeroth-order optimization using quadratic local approximations

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-30 DOI: 10.1016/j.automatica.2025.112141

Baiwei Guo , Yuning Jiang , Giancarlo Ferrari-Trecate , Maryam Kamgarpour

This paper addresses smooth constrained optimization problems with unknown objective and constraint functions The main goal of this work is to generate a sequence of feasible (herein, referred to as safe) primal–dual pairs converging towards a KKT pair. Assuming to have prior knowledge on the smoothness of the unknown functions, we propose a novel zeroth-order method that iteratively computes quadratic approximations of the constraint functions, constructs local feasible sets, and optimizes over them. We prove that this method returns an

η

-KKT pair within

O (d / η^{2})

iterations and

O (d^{2} / η^{2})

samples (where

d

is the problem dimension) while every sample is within the feasible set. Moreover, we numerically show that our method can achieve fast convergence compared with some state-of-the-art zeroth-order safe approaches. The effectiveness of the proposed approach is also illustrated by applying it to a nonconvex optimization problem in optimal control.

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引用次数: 0

Parameterized gain-constrained Kalman Filtering via singular value decomposition

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-30 DOI: 10.1016/j.automatica.2024.112103

Chenxiao Wang , Fuxing Yao , Tianshi Chen , Wei Xing Zheng , Guang-Ren Duan , He Kong

Gain-constrained Kalman filtering (KF) is an important estimation problem that has received much attention recently. It encompasses a few problems as special cases, including equality-constrained state estimation, filtering under unknown inputs, etc. In this paper, we propose a parameterized approach to gain-constrained KF by performing singular value decomposition (SVD) on the constraint condition. The filter equivalence between our results and the associated ones in the literature is established. Moreover, we show that the SVD-based approach has some computational advantages, compared to the existing methods in the literature. Specifically, on one hand, we show that with the aid of SVD, the proposed framework has computational advantages in certain situations (although it is not always the case), compared with the existing methods. On the other hand, for the case with network-induced effects, we show that the SVD-based approach is always more efficient than the existing methods, in terms of computational complexity. Finally, some numerical examples are presented to illustrate the obtained results.

{"title":"Parameterized gain-constrained Kalman Filtering via singular value decomposition","authors":"Chenxiao Wang , Fuxing Yao , Tianshi Chen , Wei Xing Zheng , Guang-Ren Duan , He Kong","doi":"10.1016/j.automatica.2024.112103","DOIUrl":"10.1016/j.automatica.2024.112103","url":null,"abstract":"<div><div>Gain-constrained Kalman filtering (KF) is an important estimation problem that has received much attention recently. It encompasses a few problems as special cases, including equality-constrained state estimation, filtering under unknown inputs, etc. In this paper, we propose a parameterized approach to gain-constrained KF by performing singular value decomposition (SVD) on the constraint condition. The filter equivalence between our results and the associated ones in the literature is established. Moreover, we show that the SVD-based approach has some computational advantages, compared to the existing methods in the literature. Specifically, on one hand, we show that with the aid of SVD, the proposed framework has computational advantages in certain situations (although it is not always the case), compared with the existing methods. On the other hand, for the case with network-induced effects, we show that the SVD-based approach is always more efficient than the existing methods, in terms of computational complexity. Finally, some numerical examples are presented to illustrate the obtained results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"174 ","pages":"Article 112103"},"PeriodicalIF":4.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Infinitesimal perturbation analysis (IPA) derivative estimation with unknown parameters

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-28 DOI: 10.1016/j.automatica.2025.112140

Hao Cao , Jian-Qiang Hu , Teng Lian , Xiangyu Yang

We consider the problem of infinitesimal perturbation analysis (IPA) derivative estimation in a setting where some parameters associated with input processes are unknown. In general, IPA estimates depend explicitly on these parameters. Therefore they need to be estimated in order to compute IPA estimates. A “brute-force” way to solve this problem is to estimate the parameters first based on historical input data and then use the estimates in place of the parameters in calculating IPA estimates. However, this method does not utilize the most recent data and thus may lose some accuracy, particularly in an environment where data are received continuously and sequentially We propose an adaptive method in which IPA estimates are computed based on the latest parameter estimates that are continuously updated as more input data are obtained. We prove that our IPA estimators are strongly consistent and have a convergence rate of

1 / \sqrt{n}

, which is the same as the traditional IPA estimators. We use the

G / G / 1

queue as an illustrative example to show how our method works in detail. Simulation experiments on several queueing systems are also provided to support our theoretical conclusions.

{"title":"Infinitesimal perturbation analysis (IPA) derivative estimation with unknown parameters","authors":"Hao Cao , Jian-Qiang Hu , Teng Lian , Xiangyu Yang","doi":"10.1016/j.automatica.2025.112140","DOIUrl":"10.1016/j.automatica.2025.112140","url":null,"abstract":"<div><div>We consider the problem of infinitesimal perturbation analysis (IPA) derivative estimation in a setting where some parameters associated with input processes are unknown. In general, IPA estimates depend explicitly on these parameters. Therefore they need to be estimated in order to compute IPA estimates. A “brute-force” way to solve this problem is to estimate the parameters first based on historical input data and then use the estimates in place of the parameters in calculating IPA estimates. However, this method does not utilize the most recent data and thus may lose some accuracy, particularly in an environment where data are received continuously and sequentially We propose an adaptive method in which IPA estimates are computed based on the latest parameter estimates that are continuously updated as more input data are obtained. We prove that our IPA estimators are strongly consistent and have a convergence rate of <span><math><mrow><mn>1</mn><mo>/</mo><msqrt><mrow><mi>n</mi></mrow></msqrt></mrow></math></span>, which is the same as the traditional IPA estimators. We use the <span><math><mrow><mi>G</mi><mo>/</mo><mi>G</mi><mo>/</mo><mn>1</mn></mrow></math></span> queue as an illustrative example to show how our method works in detail. Simulation experiments on several queueing systems are also provided to support our theoretical conclusions.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"174 ","pages":"Article 112140"},"PeriodicalIF":4.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Task-space tracking of robot manipulators via internal model principle approach

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-27 DOI: 10.1016/j.automatica.2024.112104

Haiwen Wu , Bayu Jayawardhana , Dabo Xu

This paper presents an internal model-based adaptive control method for uncertain robot manipulators, addressing the task-space asymptotic tracking problem. In the proposed scheme, the reference trajectory is assumed to be a multi-tone sinusoidal signal with unknown amplitude and frequency parameters, and the robot kinematic and dynamic parameters are considered uncertain. Unlike existing approaches that assume the reference signals are directly measurable, we propose an error feedback controller that requires only measurements of the task-space tracking error, joint position, and joint velocity. Specifically, based on the internal model principle, an internal model-based dynamic compensator is developed to reproduce the reference signals. By using the parameter linearity properties of the robot kinematics and dynamics, adaptive laws are derived to handle the unknown parameters. The stability of the closed-loop system and the asymptotic convergence of the tracking error are analyzed using output stability concepts. The effectiveness of the proposed approach is validated through numerical simulations with a three-DOF manipulator.

{"title":"Task-space tracking of robot manipulators via internal model principle approach","authors":"Haiwen Wu , Bayu Jayawardhana , Dabo Xu","doi":"10.1016/j.automatica.2024.112104","DOIUrl":"10.1016/j.automatica.2024.112104","url":null,"abstract":"<div><div>This paper presents an internal model-based adaptive control method for uncertain robot manipulators, addressing the <em>task-space</em> asymptotic tracking problem. In the proposed scheme, the reference trajectory is assumed to be a multi-tone sinusoidal signal with unknown amplitude and frequency parameters, and the robot kinematic and dynamic parameters are considered uncertain. Unlike existing approaches that assume the reference signals are directly measurable, we propose an error feedback controller that requires only measurements of the task-space tracking error, joint position, and joint velocity. Specifically, based on the internal model principle, an internal model-based dynamic compensator is developed to reproduce the reference signals. By using the parameter linearity properties of the robot kinematics and dynamics, adaptive laws are derived to handle the unknown parameters. The stability of the closed-loop system and the asymptotic convergence of the tracking error are analyzed using output stability concepts. The effectiveness of the proposed approach is validated through numerical simulations with a three-DOF manipulator.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"174 ","pages":"Article 112104"},"PeriodicalIF":4.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributionally robust LQG control under distributed uncertainty

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2025-01-27 DOI: 10.1016/j.automatica.2025.112128

Lucia Falconi , Augusto Ferrante , Mattia Zorzi

A new paradigm is proposed for the robustification of the LQG controller against distributional uncertainties on the noise process. Our controller optimizes the closed-loop performance in the worst possible scenario under the constraint that the noise distributional aberrance does not exceed a certain threshold limiting the relative entropy between the actual noise distribution and the nominal one. The main novelty is that the bounds on the distributional aberrance can be arbitrarily distributed along the whole disturbance trajectory. This is a problem for which, notwithstanding significant attention given in the recent literature, so far only relaxed or approximated solutions have been derived. We denote this Distributed uncertainty Distributionally robust LQG problem with the acronym D

^{2}

-LQG.

引用次数: 0

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